Segmentation of Retinal Features Using Hybrid BINI Thresholding in Diabetic Retinopathy Fundus Images

Abstract

In the recent era, the increasing diabetic ratio effects in producing complex retinal diseases. The early diagnosis will avoid the consequences of diabetic-related complications. So, there is an urge to develop a computerized model for identifying the retinopathy features caused due to diabetes while evading the false positives. The aim of this paper is to identify and segment the retinal features like blood vessels, optic disc in diabetic retinopathy (DR) fundus images in order to remove the non-diabetic retinopathy features which makes the detection of DR features (lesions) easier. And this is done by using a hybrid segmentation algorithm called BINI which combines the model of both binary and Niblack’s thresholding. Initially, the input image is standardized by resizing the image using bi-cubic interpolation area method. Then, the fundus image quality is enhanced using preprocessing techniques like green channel extraction, intensity channel extraction, median filtering, contrast limited adaptive histogram equalization and morphological operations. The preprocessed images are segmented to produce retinal features using the BINI algorithm. It is a novel method which hybrids both binary thresholding and Niblack’s thresholding. The performance of the segmentation methods is evaluated using the validation measures like Rand index (Ri) and Jaccard index (Ji), Precision (Pr), Recall (Rc) and F-Measure (Fm). The proposed method for segmenting the retinal features using BINI thresholding has given an accuracy of about 96.48%; it leads to an accuracy of 100% clear segmentation of the lesions of diabetic retinopathy images.